Founding GTM

Keenable builds AI infrastructure for web search and knowledge access at web scale. We are building systems and new retrieval primitives that make the world's constantly growing knowledge accessible to models.

We care much more about trajectory than polish. We hire for agency, speed, ownership, and the ability to turn ambiguity into working systems. This is a founding GTM role, which means you are the first person a lab's or a platform's engineers meet: you get Keenable running in their stack, prove it on their queries, and turn that proof into paying usage. The Head of Revenue owns the number and the commercial terms; you own the technical win and the channels that feed it.

Responsibilities

• Own the technical win with AI labs and inference platforms: get in front of the researchers and ML engineers who evaluate web search, understand how their agents and models use it, and stay close to them as that changes.

• Build the demo and the integration: stand up Keenable inside the customer's own stack (their agent, their eval harness, their model's tool calls) and show the result on their queries, not ours.

• Run evaluations and pilots to a verdict: scope the benchmark, hold the timeline, clear the technical objections, and hand the Head of Revenue a proven, sized deal to paper, or close it yourself when the terms are standard.

• Own the platform channel: integrations with inference platforms, agent frameworks and resellers, and the self-serve funnel behind them, so that usage on those channels turns into accounts.

• Bring back what labs and platforms actually need to product and engineering, and turn it into the demos and materials the next customer sees.

Requirements

• You've won technical evaluations before: labs, platforms or infrastructure teams that chose your product on a benchmark or a pilot you ran, with numbers attached.

• You can build a demo yourself. You're comfortable calling an API, wiring it into an agent or an eval, and putting the result in front of a researcher without waiting for an engineer.

• Comfortable in a conversation where the buyer is a research scientist or an ML engineer. You can hold your own on latency, recall, freshness, and cost trade-offs, and you know when to bring in Matthias or the team.

• You know the lab and inference landscape: who trains what, who serves what, which platforms and frameworks agents run on, and who we compete with for the same query.

• Want the mission: make the world's knowledge accessible to agents.

Perks

• Frontier AI infrastructure work with real technical depth and real scale.

• High autonomy, low process, and real production problems.

• Access to meaningful compute and infrastructure for experimentation.

• Competitive compensation with meaningful early-stage equity.

• A small, high-talent team with direct exposure to frontier customers.

• Premium medical, dental & vision coverage, Health Savings Account (HSA).

• 401(k) retirement plan.

• Flexible time off. We trust you to manage your time and recharge when needed.

Things you should know

• This is startup work. It is intense, ambiguous, and results-oriented.

• Our interview process reflects real work. We evaluate how you operate, not trivia.

• We move quickly when there is mutual excitement.

• Bay Area preferred. The labs and platforms we work with are here, our founders are here, and the demos that win are usually built in the customer's office.

Sample problems you might work on

• A frontier lab's agent team is evaluating three web search providers on their internal benchmark. Get access to the benchmark, build the integration in their harness, run it, and turn the result into a pilot with a volume commitment.

• An inference platform wants Keenable as the search tool behind its hosted models but hasn't committed to volume. Build the demo on their models, design the integration, and make their launch a recurring line on our revenue chart.